gms | German Medical Science

GMDS 2014: 59. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

07. - 10.09.2014, Göttingen

Analysis of familial contribution to ischemic stroke mortality: a population-based approach

Meeting Abstract

  • S. Seuchter - Universitätsklinikum Würzburg, Würzburg
  • J. Majersik - University of Utah, Salt Lake City
  • L. Cannon-Albright - University of Utah, Salt Lake City

GMDS 2014. 59. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Göttingen, 07.-10.09.2014. Düsseldorf: German Medical Science GMS Publishing House; 2014. DocAbstr. 59

doi: 10.3205/14gmds201, urn:nbn:de:0183-14gmds2016

Veröffentlicht: 4. September 2014

© 2014 Seuchter et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction: Over the years the evidence for ischemic stroke having a heritable contribution has increased, however, the contribution of genetics to stroke and its different subtypes remains uncertain [1], [2]. Familiality of ischemic stroke has been studied in 1st and 2nd degree relatives through family history and sib-pair studies, but has not been explored on a population level. The need for high quality, large genetic epidemiologic studies of stroke is evident [3], [4], [5]. The aim of our study was to use the Utah Population Database (UPDB) to search for significant familial clustering as evidence of a genetic contribution to ischemic stroke (IS).

Methods: The UPDB is a unique data resource comprising the Utah genealogical database linked to electronic medical records and records of vital statistics (e.g. death and birth certificates, driver’s licenses) [6]. In the last three decades, the UPDB has been used to define familial clustering for many phenotypes based on three different methods, developed by University of Utah researchers [6]. Those methods are: i) a test for excess relatedness among affected individuals using the Genealogical Index of Familiality (GIF), ii) estimation of relative risks (RR) for a phenotype in relatives of affected individuals; and iii) identification of pedigrees with statistical excess of affected cases. We studied 15,798 individuals with high quality genealogy data whose death certificate indicated that ischemic stroke contributed to death (based on ICD codes).

Relative risks for first-, second-, and third-degree relatives of ischemic stroke cases were assessed. For the GIF statistic, [6] we compared the observed relatedness of IS cases to the expected average relatedness estimated from 1,000 independent sets of matched controls. The latter were chosen randomly from the set of 2.3 million individuals with at least three generations of genealogy records in the UPDB (matching was based on sex, 5-year birth cohort, and birth in Utah or elsewhere).

Results: We observed significantly elevated RRs for IS in first-, second-, and third-degree relatives of IS cases as well as in selected subsets of IS that differentiated between ischemic and hemorrhagic stroke based on ICD coding. The GIF analysis confirmed the RR results: significant excess relatedness was observed up to a genetic distance of 7, which represents second cousins. Multiple specific Utah high risk IS pedigrees were identified with at least 3 related affected cases.

Discussion: Self-reported family history data may be highly inaccurate due to multiple sources of bias. Access to death certificate data linked to respective genealogy records provides a valuable resource for research in genetic epidemiology. Our study used well-established methods to consider a hypothesized genetic contribution to risk, and examined both close and distant relationships. All three analytic methods support a genetic contribution to ischemic stroke mortality. Further research will focus on characterization of the high risk pedigrees with statistical excess of affected individuals.

Partial support for all data sets within the Utah Population Database (UPDB) was provided by Huntsman Cancer Institute, University of Utah and the Huntsman Cancer Institute's Cancer Center Support grant, P30 CA42014 from National Cancer Institute.


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